Neuronal profiling

ABSTRACT

The present invention provides automated methods for cell body extension analysis, software for carrying out such methods, and detection devices comprising such software.

BACKGROUND OF THE INVENTION

Drugs that promote neuron development, and thus neurite outgrowth, willbe of use for treating a wide variety of diseases and trauma that resultin neuropathy and nerve injury, including but not limited to spinal cordinjury, neuropathy resulting from diseases such as diabetes and stroke,Parkinson's disease, and other forms of dementia including Alzheimer'sdisease.

The most frequently used methods for neurite outgrowth quantificationand analysis are based on manual or, in some cases, on semiautomaticimaging tools for neurite tracing. Automated neurite tracing, however,is a prerequisite for the use of neurite outgrowth analysis in highcontent screening. Automated tracing of neurites in a population ofcells is difficult due to ambiguities caused by neurite intensityvariations and multiple neurite crossings, or tangles, which are createdwhen a neurite cross itself or another neurite.

Thus, improved methods for analyzing neurites and neurite outgrowthwould be of great value in the art.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides an automated method forcell body extension analysis, comprising:

(a) obtaining a cell body image and a cell body extension image fromcells in a population of cells comprising cells with cell bodyextensions, wherein the cells and cell body extensions are labeled withreporters, and wherein the cell body image and the cell body extensionimage are obtained from signals emitted by the reporters;

(b) processing the cell body image to create a cell body mask and tovalidate cell bodies in the cell body mask, and processing the cell bodyextension image to create a cell body extension mask, which is processedto produce a cell body extension skeleton;

(c) untangling cell body extensions, wherein the untangling comprises:

-   -   (i) identifying end points on the cell body extension skeleton;    -   (ii) identifying and removing critical points from the cell body        extension skeleton; and    -   (iii) tracing the end points and the critical points to untangle        cell body extensions in the cell body extension skeletons; and

(d) assigning untangled cell body extensions to the validated cellbodies.

In a preferred embodiment, the cell body extensions comprise neurites.

In a second aspect, the present invention provides a machine readablestorage medium comprising a set of instructions for causing a detectiondevice to carry out the methods of the first aspect of the invention.

In a third aspect, the present invention provides detection devicescomprising the computer readable storage media of the second aspect ofthe invention.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart outlining a sequence of image processing andanalysis steps employed in one embodiment of the methods of theinvention.

FIG. 2 is an example of a nuclear image obtained according to themethods of the invention.

FIG. 3 is an example of a cell body/neurite image obtained according tothe methods of the invention.

FIG. 4 is an example of a cell body mask obtained according to themethods of the invention.

FIG. 5 is an example of a neurite mask obtained according to the methodsof the invention.

FIG. 6 is an example of a neurite skeleton obtained according to themethods of the invention.

FIG. 7 is an example of a neurite skeleton after removal of criticalpoints.

FIG. 8 is a flowchart of exemplary image processing steps for obtainingneurite masks and neurite skeletons.

FIG. 9 is a flowchart of exemplary image processing steps for neuritetracing.

FIG. 10 is a diagram of one method for identifying critical points inthe neurite skeleton.

FIG. 11 is a diagram of one embodiment for determining which neuritesegment to add to the neurite skeleton trunk.

FIG. 12 is a diagram of an exemplary neurite tracing where the neuritetouches more than one neuron cell body.

FIG. 13 is a flowchart for neurite association with cell bodiesaccording to one embodiment of the invention.

FIG. 14 is a diagram of segmentation of the neurite mask according toone embodiment of the invention.

FIG. 15 is an exemplary distribution of primary cell-level featuresmeasured over a sample of many untreated cells (solid curve) and treatedcells (dashed curve).

DETAILED DESCRIPTION OF THE INVENTION

All publications, patents and patent applications cited herein arehereby expressly incorporated by reference for all purposes.

As used herein, the singular forms “a”, “an” and “the” include pluralreferents unless the context clearly dictates otherwise. For example,reference to a “reporter” means one or more reporters.

In a first aspect, the present invention provides automated methods forcell body extension analysis, comprising:

(a) obtaining a cell body image and a cell body extension image fromcells in a population of cells comprising cells with cell bodyextensions, wherein the cells and cell body extensions are labeled withreporters, and wherein the cell body image and the cell body extensionimage are obtained from signals emitted by the reporters;

(b) processing the cell body image to create a cell body mask and tovalidate cell bodies in the cell body mask, and processing the cell bodyextension image to create a cell body extension mask, which is processedto produce a cell body extension skeleton;

(c) untangling cell body extensions, wherein the untangling comprises:

-   -   (i) identifying end points on the cell body extension skeleton;    -   (ii) identifying and removing critical points from the cell body        extension skeleton; and    -   (iii) tracing the end points and the critical points to untangle        cell body extensions in the cell body extension skeletons; and

(d) assigning untangled cell body extensions to the validated cellbodies.

As used herein, the “cell body extensions” can be any type of extensionfrom a cell body that can become “tangled” for purposes of imageanalysis of the cell body extensions from a given cell body. Exemplarycell body extensions are neurites and pseudopodia. Image analysis ofpseudopodia is useful, for example, in screening of pharmacologicalagents that either stimulate or inhibit pseudopodia formation. A moredetailed discussion of the uses of neurite analysis is provided below.

In a preferred embodiment, the methods of the first aspect of theinvention comprise:

(a) obtaining a cell body image and a neurite image from cells in apopulation of cells comprising neurons, wherein the cells and neuritesare labeled with reporters, and wherein the cell body image and theneurite image are obtained from signals emitted by the reporters;

(b) processing the cell body image to create a cell body mask and tovalidate cell bodies in the cell body mask, and processing the neuriteimage to create a neurite mask, which is processed to produce a neuriteskeleton;

(c) untangling neurites, wherein the untangling comprises:

-   -   (i) identifying end points on the neurite skeleton;    -   (ii) identifying and removing critical points from the neurite        skeleton; and    -   (iii) tracing the end points and the critical points to untangle        neurites in the neurite skeletons; and

(d) assigning untangled neurites to the validated cell bodies

While the remaining disclosure focuses on neurite analysis, those ofskill in the art will recognize that the methods of the invention applyto analysis of other cell body extensions, as discussed above.

The imaging in step (a) and the image processing in step (b) can becarried out in any order. Thus, the cell body image can be obtainedfirst; the neurite image can be obtained first, or the two images can beobtained at the same time. Similarly, the cell body mask can be obtainedfirst, the neurite mask and neurite skeleton can be obtained first, orthe masks (and neurite skeleton) can be obtained simultaneously.

For example, when the cell body image and neurite image are obtained(acquired) independently (as discussed in more detail below), any orderof processing/analysis of cell body image and neurite image can be used.When a combined cell body image/neurite image is used (as discussed inmore detail below), it is preferred that the cell body image isprocessed first and validated cell bodies identified prior to neuriteimage processing.

The methods of the invention provide a solution to the problem ofautomated tracing of neurites in a population of cells, where neuritesoverlap. The methods provide for identification of the origin and growthdirection of the neurites, allowing the accurate untangling of neuritestructures in two-dimensional images without artificial simplificationof neurite structure or limitation of neurites to cell body zones ofinfluence (ZOI) or equidistant regions around cell bodies. The methodsof the invention also provide for significantly increased speed inautomated neurite analysis.

As used herein, “a population of cells comprising neurons” includes anycell population comprising neurons of any type, including, but notlimited to, primary cultures of brain cells that contain neurons,isolated cell cultures comprising primary neuronal cells, neuronalprecursor cells, tissue culture cells that are used as models of neurons(such as PC12 cells, which are a neoplastic neuronal cell line clonedfrom rat pheochromocytoma), or mixtures thereof. As will be apparent tothose of skill in the art, the population of cells can include bothneurons and non-neuronal cells, thus permitting the use of primarycultures, etc.

As used herein, the term “neurite” refers to any processes and/orstructures that grow from a neuron's cell body including but not limitedto axons, dendrites, neurites, intermediate segments, terminal segments,filopodia and growth cones.

The reporters can be any molecules, compound, or substance that can bindto a target of interest for the purposes of the invention and which canemit signals capable of being processed to obtain a neuron image and aneurite image. Examples of such reporters include, but are not limitedto radioisotope markers, fluorescent markers, luminescent markers, andelectrochemical markers. In a preferred embodiment for high contentscreening purposes, fluorescent markers are used. Such fluorescentmarkers include, but are not limited to, dyes, quantum dots, fluorescentmolecules (including but not limited to green fluorescent protein andrelated fluorescent proteins), and fluorescently labeled molecules(including but not limited to fluorescently labeled antibodies andligands). Such fluorescent reporters may be expressed as fluorescentreporters by the cells, added to the cells as fluorescent reporters, orfluorescently labeled by contacting the cell with a fluorescentlylabeled molecule, compound, or substance that binds to the reporter,including but not limited to dyes and primary or secondary antibodiesthat bind to the reporter. Fluorescent reporters may be expressed bytransfected cells or added to the cells via non-mechanical modesincluding, but not limited to, diffusion, facilitated or activetransport, signal-sequence-mediated transport, and endocytotic orpinocytotic uptake; or combinations thereof, at any time during thescreening assay. Mechanical bulk loading methods, which are well knownin the art, can also be used to load fluorescent probes into livingcells (Barber et al. (1996), Neuroscience Letters 207:17-20; Bright etal. (1996), Cytometry 24:226-233; McNeil (1989) in Methods in CellBiology, Vol. 29, Taylor and Wang (eds.), pp. 153-173). These methodsinclude, but are not limited to, electroporation and other mechanicalmethods such as scrape-loading, bead-loading, impact-loading,syringe-loading, hypertonic and hypotonic loading. Additionally, cellscan be genetically engineered to express fluorescent reporters, such asgreen fluorescent protein (GFP), coupled to a protein of interest(Chalfie and Prasher U.S. Pat. No. 5,491,084; Cubitt et al. (1995),Trends in Biochemical Science 20:448-455). Fluorescently labeledantibodies are particularly useful reporters, due to their high degreeof specificity for attaching to a single molecular target in a mixtureof molecules as complex as a cell. The fluorescent reporters that agiven cell possesses may all be introduced to the cells via the sametechnique, or via any combination of such techniques.

Exemplary targets for the reporter molecules include, but are notlimited to, neuron-specific targets, such as neurofilaments,βIII-tubulin, and neurotrophic factors such as the ciliary neurotrophicfactor (CNTF), all being neuron-specific antigens and proteins. In otherembodiments, the target can include, but is not limited to:

Cytoplasm: The cytoplasm can be reported on, for example, with anystandard cytoplasmic stain. Examples of such stains are CMFDA(chloromethyl fluorescein diacetate), or CMTMR (chloromethyltetramethylrhodamine) (Molecular Probes). Alternatively, the cells canbe engineered to express an autofluorescent protein such as GreenFluorescent Protein (GFP). The expressed GFP in the cytoplasm allows thecell body and/or neurite to be visualized.

Membrane: The membrane can be reported on, for example, by any standardlipid dye or by labeled membrane proteins. Examples of such standardlipid dyes include, but are not limited to, diI(dioctadecylindocarbocyanine) (Molecular Probes). To fluorescently labelmembrane proteins, one can use either immunofluorescence against cellsurface proteins (using standard immunofluorescent staining techniques)or a fluorescent ligand that binds a membrane protein. This strategy canserve a dual purpose in that, in addition to identifying the neuronshape and neurites, it can also be used to specifically and selectivelyidentify neurons from a mixed brain culture. Examples of neuron specificmarkers that are on the membrane are the various neurotrophic factors.For example, indirect immunofluorescence against the ciliaryneurotrophic factor CNTF on the surface of neurons can delineate thearchitecture of the neuron.

Cellular Proteins: As noted above, as well as, for example, cytoskeletalproteins that are not neuron specific.

A combination of all of these targets can also be used to betteridentify the neurons and neurites.

In one embodiment, the cell body and neurite images are obtained fromsignals emitted by the same reporter, which can include any of thereporters discussed above. In this embodiment, the cell body and neuriteimages are combined into a single image, and subsequent mask creationresults in a combined cell body-neurite mask. The cell body and theneurites can be combined into a single image because both contain thesame reporter. The cell body mask and the neurite mask (see below) areseparated during image analysis using any appropriate image processingtechnique, including but not limited to shape-sensitive image processingmethods, such as erosion followed by conditional reconstruction (See,for example, U.S. Pat. No. 6,986,993) and morphological opening (PierreSoille, Morphological Image Analysis: Principles and Applications,Springer-Verlag, Berlin, 2004, p. 105). Thus, the methods of theinvention only require use of a single imaging channel. Any of thereporters/targets described above can be used in this embodiment.

In an alternative embodiment, the cell body and neurite images areobtained from signals emitted by different reporters. In thisembodiment, the cell body image and the neurite images are obtainedseparately, and the separate images are separately processed to obtainthe cell body mask and the neurite skeleton mask, respectively.Subsequent steps in the two embodiments are preferably the same. In thisembodiment, two imaging channels are required for high content imageanalysis, as spectrally distinguishable reporters are used to report onthe cell body and the neurites, respectively. Any of thereporters/targets described above can be used in this embodiment.

Creation of cell body mask from the cell body image and neurite maskfrom the neurite image can be accomplished using standard techniques inthe art. For example, the image can be scanned pixel-by-pixel, assigninga value 0 to background pixels and a value 1 to object pixels. Thisassignment can be done based on ranges of the pixel values (e.g., objectpixels may have significantly larger values than background pixels). Themask can embody the shapes and sizes of the objects, and theirlocations, but does not embody features like texture or perspective (byshading). Such a mask can be laid on top of the original image and cutaround all the “1” pixels to separate the objects from the background.(Rafael Gonzalez and Richard Woods, Digital Image Processing,Addison-Wesley, Reading, Mass., 1992, p. 443; Bernd Jähne, Digital ImageProcessing, 5^(th) edition, Springer-Verlag, Berlin, 2002, p. 427.) Theneurite mask can then be used to create a neurite skeleton, as discussedin detail below.

Other imaging channels can be added as desired, for example, when usingother reporters that are spectrally distinguishable from the reporterswhose signals are used to obtain the cell body and neurite images. Inone embodiment, a nuclear reporter (a reporter that reports on thenucleus) can be used, and the method further comprises obtaining anuclear image from the signals emitted by the nuclear reporter. In thisembodiment a further channel is used to obtain images from the nuclearreporters; this requires the use of nuclear reporters that arespectrally distinguishable from the reporters whose signals are used toobtain the cell body and neurite images. In this embodiment, it isfurther preferred to process the nuclear image to obtain a nuclear mask,and further preferred to segment nuclear mask objects (ie: objectsdefined by nuclear masks). Segmentation of the nucleus mask objectsimproves the accuracy of the cell count by separating touching objects.The method can further comprise computing nucleus features to validateidentified nuclei (according to user-defined parameters).

Analysis of nuclei provides additional information that can be useful inits own right, and is also useful in the subsequent analysis of cellbodies. For example, creating the cell body mask may be complicated byholes (dark regions) in the neuron image caused by the displacement ofthe reporters by the nucleus. It is preferred that such holes, ifpresent, are filled. Since these holes correspond to nuclei, they can befilled, for example, by pasting objects from the nuclear mask. Thus, ina further embodiment, the methods of this first aspect further comprisecomparing the nuclear mask to the cell body mask to fill holes in thecell body mask. It is further preferred that this comparison comprises aco-localization test between nucleus objects and cell body objectsbefore pasting nuclear objects that co-localize with holes in the cellbody mask. The co-localization test is preferably performed beforepasting because there may be objects in the nucleus mask that do notcorrespond to holes in the cell body mask. If one of these objects werepasted into the cell body mask, a new cell body mask object could becreated. The co-localization test verifies that a hole is being filledin an existing cell body mask object, and that a new cell body maskobject is not being created.

In a further optional embodiment, an exemplary co-localization testbetween nuclear objects and holes in the cell body mask comprises:

(i) creating a test ring around each nucleus mask object;

(ii) measuring contiguities between the test rings and cell body maskobjects; and

(iii) pasting nucleus mask objects that pass a contiguity test into thecell body mask.

The goal of this embodiment is to create a ring around each nuclear maskobject that is one pixel wide and is bigger than the hole in the cellbody mask, but not so big that it is bigger than the cell body maskobject that contains the hole. This test ring can be created, forexample, by dilating the boundary of the nuclear mask objects. Becauseof resolution effects in the image, the number of dilations of theboundary is proportional to the magnification of the microscopeobjective used. The contiguity between a nuclear mask object and thecell body mask can be measured, for example, as the fraction of thenuclear object's test ring pixels that overlap pixels in the cell bodymask. If the contiguity is sufficient, it is assumed that pasting thenuclear mask object into the cell body mask will fill an existing holein the latter, and not create a new cell body mask object. The level ofcontiguity required for pasting can be specified as a userinput-parameter. Those of skill in the art will recognize that otherco-localization tests can be utilized in conjunction with the methods ofthe invention.

This hole filling method is simple and convenient since there are twoimaging channels: the first contains plugs and the second containsholes. However, there may be more plugs than holes, so a co-localizationtest is preferable to prevent the filling of nonexistent holes (whichwould create false objects in the second channel). It may be possible tofill the holes without pasting plugs from a different imaging channel.In this case the nucleus imaging channel and the hole-fillingco-localization test would be obviated. For example, the dark regionswithin cell bodies are slightly brighter than the spaces between cellbodies because they are not really empty, since the dark regions areenclosed within the cell membrane (and are viewed through it) and areoccupied by something (e.g., nucleus). The cell membrane and nucleus maynot contain the reporter, but still will have a small signal from theseanyway. Thus, the signal from the dark regions within the cells isgreater than the signal from the true background regions between cells.As a result, one can use the strong signals from the bright regions ofthe cell bodies as markers to localize the search for the weak signalsthat differentiate between dark cellular regions and the backgroundbetween cells. (See, for example, Bernd Jähne, Digital Image Processing,5^(th) edition, Springer-Verlag, Berlin, 2002, p. 432.)

Based on the teachings herein, those of skill in the art will recognizethat other co-localization and hole-filling techniques can also be usedin accordance with the present invention.

Use of a nuclear channel may also be preferred for various otherpurposes, including but not limited to measuring theneuronal/non-neuronal population ratio in a mixed culture of cells.Channels can also be added, for example to distinguish cells based onthe presence of other reporters; for example, neurotransmitters orcellular dyes.

In embodiments where nuclear reporters are used, the nucleic acid dyeHoechst 33342 is a preferred nuclear reporter to identify the nuclei ofall the cells in the cell population. However, other nuclear reporterscan also be used. Nucleic acid fluorescent stains are of two kinds:those that can cross the plasma membrane of live cells, and those thatare membrane impermeant. Examples of membrane permeant nucleic acidstains include DAPI, dihydroethidium, hexidium iodide, Hoechst 33258,and the SYTO® dye series (Molecular Probes). To label nuclei withmembrane-impermeant dyes, the plasma membrane has to be permeabilized.Examples of membrane-impermeant nucleic acid dyes include cyaninenucleic acid labels such as TOTO®, YOYO®, BOBO™, POPO™, TO-PRO®,YO-PRO®, BO-PRO™ and PO-PRO™ (Molecular Probes), ethidium analogs suchas ethidium-acridine heterodimer, ethidium bromide, ethidium diazide andethidium homodimers 1 and 2, propidium iodide, and the green nucleicacid stain SYTOX® (Molecular Probes).

In a preferred embodiment, creation of cell body objects comprisessmoothing of cell body object boundaries. In a preferred embodimentwhere a combined cell body-neurite mask is obtained, the smoothingprocess comprises removing neurites from the combined cell body-neuritemask, to facilitate cell body segmentation. Neurite removal can beaccomplished by any appropriate means, including but not limited tousing a sequence of erosions and dilations (see FIG. 4). In embodimentswhere hole-filling is carried out, neurite removal can occur afterhole-filling, particularly if neurite removal is based on erosion. Basedon the teachings herein, those of skill in the art will recognize thatother neurite removal techniques can also be used in accordance with thepresent invention, including those that do not require erosion, andwhere the order of hole-filling and neurite removal is optional.

In a further preferred embodiment, following neurite removal, cell bodyobjects are segmented to separate cell bodies that might touch oroverlap. Such segmentation can be carried out by any appropriate method,including but not limited to those disclosed in the examples thatfollow. In one embodiment, the touching cell body mask objects aresegmented based on either the shape of their aggregate boundary, orbased on the distribution of intensity within their aggregate boundary.The user can specify which method is used via the input parameters. Theuser also has the option, again through the input parameters, of usingthe valid nuclear mask objects to improve the segmentation results. Forexample, if it is known that each neuron contains exactly one nucleus,using the nuclear mask objects can prevent the segmentation of anagglomerated cell body object into objects that contain multiple, or no,nuclei. Also use of the nuclear mask objects prevents segmentation of anagglomerated cell body object along lines that would cross, or split anuclear object. In a further preferred embodiment, the segmented cellbodies are then compared with the cell body validation criteria in theassay protocol, thus validating cell body objects as neuron cell bodies(ie, “validated cell bodies”). As used herein, the term “validated”means identified as meeting user-defined selection/rejection criteria,such as size, shape, and/or intensity.

Based on the teachings herein, those of skill in the art will recognizethat other segmentation techniques can also be used in accordance withthe present invention. In one further embodiment, segmentationcomprises:

-   -   1. Objects from the nucleus mask (if obtained), and or        co-localization with nuclei, can be used in this process, as        selected by the user;    -   2. Cell body segmentation seeds are identified via any technique        known to those of skill in the art using: either (a) nucleus        mask objects; or (b) seeds derived from cell body shape and/or        intensity distribution. As used herein, a “seed” is the core of        an object. Obtaining seeds is well within the level of skill of        those in the image analysis field;    -   3. Cell bodies are segmented using the seeds obtained in #2,        with or without gradients derived from neuron cell body shape        and/or intensity distribution to obtain neuron cell body mask        objects. As is well understood by those of skill in the image        analysis field, such gradients determine how fast a seed grows        in various directions; if there is no gradient, a seed grows        into a circular object. For example, by using gradients related        to the boundary shape a seed corresponding to a larger object        can be made to expand faster than a seed corresponding to a        smaller object. This will result in a more accurate dividing        line between touching objects of differing sizes As discussed        above, using nuclear mask objects can prevent doing        non-biologically reasonable segmentation—creating cell bodies        without nuclei or having the cell body dividing line split a        nucleus. Using gradients can improve the accuracy of the        division by allowing other factors to influence the “geometry”        of the split. (See, for example, Pierre Soille, Morphological        Image Analysis: Principles and Applications, Springer-Verlag,        Berlin, 2004, p. 268.);    -   4. Cell body objects are segmented: Incomplete neurite removal        and neuron cell body segmentation can create isolated objects in        the cell body mask that may not be cell bodies. These artifacts        are identified using a nucleus co-localization test and are        removed from the cell body mask.

As will be apparent to those of skill in the art, any type ofco-localization test can be used. In one-non-limiting example, thisprocess comprises (a) creating a test ring inside each nucleus maskobject; (b) measuring contiguities between test rings and cell body maskobjects; and (c) those objects that fail the contiguity test areeliminated. The test ring used here is different than the one used inthe fill holes procedure (see above). Prior to hole filling, theco-localization is tested externally to the nucleus mask object, butsubsequent to hole filling it can be tested internally to the nucleusmask object.

Thus, in various non-limiting embodiments, the methods of this firstaspect can optionally comprise two distinct co-localization tests. Thefirst co-localization test (described above) is between an object and ahole (nucleus and hole in a cell body), thus the test ring is outsidethe nucleus. The second co-localization test is between two objects(nucleus and cell body) and the test ring is inside the nucleus (thesmaller of the two objects). Given a set of nucleus objects and a set ofcell body objects, the user can specify how many nuclei, if any, arerequired to belong to each cell body. The second co-localization testcounts the number of nuclei that belong to each cell body. Cell bodyobjects that fail to have the required number are eliminated.

In a further embodiment, the cell body mask is used by other imagingchannels to detect and measure additional reporters that might also bein the cell, and thus to obtain further information about the neuron inquestion For example, intensity measurements obtained from secondarychannels (primary channels are those where cell body/neurite/nucleusmasks are defined) can be used for “gating”—selection of one or moresubpopulations of cells on the basis of measurements from the secondarychannels. In one non-limiting example of gating,: a first channel(nucleus) and a second channel (cell body) are used for cell bodydetection and validation; channels 3, 4, 5, and 6 can then be used forgating selection of subsets of cells with cell bodies that meet cellbody selection/rejection criteria as defined by the user for theseadditional channels. The user can thus set the criteria so as toidentify different subpopulations of cells in each additional channel.These measurements of additional reporters can be used, for example, toidentify specific sub-populations of neurons in the a mixed cellpopulation, or to eliminate certain neurons from the analysis process.It will be understood by those of skill in the art that this step can becarried out at any time during the method subsequent to obtaining thecell body mask. As will also be apparent to those of skill in the art,the approximate region of the cell body mask can be used in otherimaging channels, by physically aligning or spatially registering theimage channels. Thus, the method may further comprise modifying thechannel (ie: dilating or eroding the cell body mask in a particularchannel, for example) to improve the accuracy of the results.

As will be understood by those of skill in the art, each channel may(but need not necessarily) correspond to a different filterconfiguration (wavelength). One could have, for example, channel 1 atwavelength A, channels 2 and 3 at wavelength B and channels 4 and 5 atwavelength C. In that instance, the settings from channel 2 may differfrom channel 3 (and channel 4 from channel 5) as to a parameter otherthan wavelength For example, a neurite image may be acquired in morethen one channel using the same wavelength and with a focal planeoffset. Combining multiple channels with different focal planes couldallow improved tracing of neurites that extend through different focalplanes

In a further embodiment, the cell bodies can then be selected foranalysis based on the gating process. After cell bodies are selected foranalysis, nuclei can again be validated, if desired, to reject any thatmight belong to cell bodies that were not selected for analysis.

The neurite mask is created from the neurite image using any suitabletechnique in the art, such as those masking techniques described above.An exemplary technique for neurite mask creation is provided in FIG. 8.Neurite images are obtained from signals from the reporters, asdescribed above. The neurite mask is then processed to produce a neuriteskeleton. The neurite skeleton is used for neurite tracing, to measureneurite length, and to identify branch and cross points (see below). Theneurite mask is used to measure neurite width, area, and for neuriteintensity measurements (see below). The cell body masks are preferablyremoved from both the neurite mask and its neurite skeleton (see FIG. 6)before these analyses.

In various embodiments for neurite mask production, the neurites withinthe mask might have small holes. A hole in a mask object could cause aloop in the medial axis (the medial axis will have to encircle the hole,and cannot go through it). In a neurite, a hole could thus create a loopin the neurite skeleton. This loop could be interpreted as a pair offalse branch points. Thus, in a further optional embodiment, neuriteholes are filled. In this embodiment, the holes are preferably filledprior to creation of the neurite skeleton using any appropriatetechnique; such hole-filling techniques are well known to those of skillin the image analysis arts.

In a preferred embodiment, the neurite skeleton is produced (alsoreferred to herein as “skeletonization”) by: reducing the neurite maskto lines that correspond to any boundary or internal axis of theneurite. As used herein the term “neurite skeleton” refers to acontinuous structure that represents the shape of the neurite, includinglocations of end points and critical points, and the sequence of neuriteskeleton segments that connect them. Neurites are filaments with,practically speaking, parallel boundaries. Thus, using either boundary,or any internal axis, such as the medial axis, will give the same resultsince these will all be parallel. In a further preferred embodiment, thelines comprise single pixel-width lines, which facilitate the untanglingprocess. Based on the teachings herein, those of skill in the art willrecognize that other skeletonization techniques can also be used inaccordance with the present invention.

In a further preferred embodiment, the cell body masks are preferablyremoved from the neurite skeleton (where combination cell body-neuritemasks were obtained) to identify the points where the neurites areattached to the cell bodies (initial points). Critical points where theneurite skeleton branches or crosses itself are also identified and areremoved from the neurite skeleton to disassemble it into independentneurite segments.

As used herein the term “end points” refers to initial points (neuriteattachment points to the cell body) or terminal points, which are thefree ends of the neurites.

As used herein the term “critical points” refers to branch points (fromsingle neurite) or cross points (one neurite crossing itself, or two ormore neurites crossing each other). Critical points act as nodesconnecting neurite skeleton segments.

In some cases, the skeletonization process may be incomplete. Thus, in afurther embodiment, skeletonization optionally further comprises one ormore of the following:

(i) pruning the neurite skeletons to remove single-pixel branches;

(ii) thinning the neurite skeletons to one pixel thick at all points;

(iii) identifying the critical points as crossing or a branch pointsbased on a number of neurite skeleton segments that meet at each point;and

(iv) smoothing discontinuities at neurite crossing points so thesepoints are identified as a single cross point instead of a pair ofclosely spaced branch points.

Following skeletonization, the methods of the invention further comprisetracing the end points and critical points to untangle the neurites inthe neurite skeleton. In a preferred embodiment, this process comprises:

(i) selecting an initial point and attaching its neurite skeletonsegment to form a first neurite skeleton segment;

(ii) adding to the first neurite skeleton segment a first critical pointtouching the first neurite skeleton segment;

(iii) adding to the first critical point a second neurite skeletonsegment that minimizes a kink angle at the first critical point andwhich is in the overall growth direction of the neurite;

(iv) repeating steps (ii)-(iii) a desired number of times, wherein thetracing serves to assign neurites to validated cell bodies.

In a preferred embodiment, steps (ii)-(iii) are repeated until a firsttraced neurite skeleton is complete. In a further embodiment, steps(i)-(iii) are repeated for one or more additional initial points on acell body, more preferably for all of the initial points on a cell body.In a further preferred embodiment, the steps are continued until theadditional traced neurite skeletons are complete.

As used herein, “tracing” means following the neurite from the initialpoint to the terminal point, through the critical points.

As used herein, “untangle” means determining which neurites belong towhich neuron cell bodies, and which neurite segments belong to whichneurites.

As used herein, the term “kink angle” means the deviation between thedirection of the skeleton and the added neurite segment at the criticalpoint.

As used herein, the term “neurite skeleton segment” refers to portionsof the neurite skeleton from either an end point (or initial point) to acritical point, or from one critical point to another critical point.

The overall structure of the neurite skeleton is like a tree, with thecritical points acting as the nodes that connect the neurite segments,or branches. The reassembly process begins with the root of the tree(initial point) and sequentially adds neurite segments and criticalpoints until the terminal point, or end, of each branch is reached.After the addition of each critical point, the method attempts to add aneurite segment, which can be selected from a set of, possibly, severalalternatives. In one embodiment, initial points are identified attouching point between the neurite skeleton and the cell body masks.This results in neurites that come out of the cell body at a wide rangeof angles, including tangential for neurites that just graze the cellbody surface. In an alternative embodiment, the number of initial pointsis restricted to those growing radially outward from the cell body in arestricted range of allowed angle. In this embodiment, it is furtherpreferred that a touching point is considered an initial point only ifthe direction of its attached neurite skeletal segment is within anallowed angle range; measured, for example, from the radial directiondefined by the center of the cell body and the touching point. Neuritesegments that touch cell bodies with angles outside the user-definedrange would not form initial points. As used herein, attachment pointsare a subset of touching points. Touching points are identified and areevaluated, based on the angle the touching neurite makes with the cellbody boundary. If the touching neurite is more radial than tangential,the touching point is considered an attachment point. The user canadjust the criterion via the input parameters.

When neurites cross each other, there is continuity in their individualdirections. When neurites branch, one of the branches may continue inthe original direction (main branch) and the other may go off in a newdirection (secondary branch). The distinction between main and secondarybranches is not critical to the methods of the invention. In a preferredembodiment, when adding a neurite segment to a critical point, the onethat minimizes the kink angle (deviation between the direction of theneurite skeleton and the added neurite segment at the critical point) isselected.

The neurite trees grow radially outward from the cell body. Consequentlyneurite branching is biased in the direction of growth. (Branch anglesare on average less than 90°). A theoretical ideal maximum kink angle is90°, but lesser or greater maximal kink angles can be used in thepractice of the current invention. Thus, maximal kink angles caninclude, but are not limited to, 75°, 80°, 85°, 90°, 95°, 100°, 105°,110°, 115°, 120°, 125°, and 130°.

The kink angles at each critical point can be computed, for example, bydetermining the orientations of the segment ends that meet at thatpoint, using any suitable technique in the art. Based on the teachingsherein, those of skill in the art will recognize that other techniquesfor determining kink angles can also be used in accordance with thepresent invention.

Multiple neurons are present in most practical analysis contexts, and inthese settings it is possible for a neurite to touch more than one cellbody. Thus, the methods involve analysis to identify the cell body atwhich that neurite originates. In a preferred embodiment, assigningneurites to validated cell bodies comprises

(i) tracing the neurite skeletons using available initial points,critical points and neurite skeleton segments;

(ii) tracing a neurite skeleton for each validated cell body that aneurite touches; and

(iii) assigning the neurite to the validated cell body on which theneurite would have the overall longest length.

If a neurite touches more than one cell body, a neurite skeleton istraced for each of these cell bodies (i.e., it will be multiply traced,preferably using the rules described for the preferred embodimentsabove). Since neurite branching is biased in the direction of growth,the trace that follows the growth direction will be the longest. This istrue because that trace will include the largest number of branches.Other tracing directions will encounter branches that do not satisfy themaximum kink angle criterion, and these branches can be excluded. Thelongest length criterion is only true if the neurite has branches. Ifthe neurite does not have branches, all tracing directions will yieldthe same length. In cases of multiple realizations of identical length,one of the realizations can be selected at random, or via any othercriteria deemed useful by the user.

At this point the neurite skeleton is untangled, while the neurite maskremains in its original state. In one optional embodiment, propagationof the untangled neurite skeleton to the neurite mask can be used toseparate the neurite mask into untangled components and preserve theassignments to valid cell bodies. An example of this process isillustrated in FIG. 14. In this example, the traced and labeled skeleton(labeled means that all pixels belonging to object 1 have a value=1, allpixels belonging to object 2 have a value=2. . . ) is superimposed onthe original unlabeled neurite mask (unlabeled means that all objectpixels=1 and all background pixels=0). Then the labeled skeleton pixelsare dilated, changing the neurite mask pixel values (all=1) to those ofthe labeled skeleton (1, 2, . . . ). Thus the neurite mask becomeslabeled. As FIG. 14 also illustrates, the labeled parts of the neuritemask are also separated (do not overlap), and thus are untangled. In thefigure, the solid mask (e.g., label=1) is one neurite and the shadedmask (e.g., label=2) is a different neurite, and they can be measuredindependently of the other. In a further optional embodiment,uncontrolled dilation of the skeleton into the neurite mask is preventedusing any suitable technique. An example of this is also provided inFIG. 14; see the left branch of the neurite mask in FIG. 14. Thedilation does not completely fill this left branch because regions thatdo not contain a skeleton are not filled. Based on the teachings herein,those of skill in the art will recognize that other propagationtechniques can also be used in accordance with the present invention.

Thus, in a preferred embodiment, the method further comprisestransferring the untangled neurite skeletons to the neurite mask, whileimposing on the neurite mask the separation achieved in neurite skeletontracing; the neurite mask is then truncated at the neurite skeletonends. The features of individual neurites can now be measured and usedto validate/select neurites for inclusion in the analysis. Subsequent toselection of the valid neurites, the untangled neurites are preferablyre-scanned to remove cross points that correspond to invalidatedneurites and to remove branch points that violated the maximum kinkangle criterion.

The resulting traced neurites are independent (do not overlap eachother), are assigned to specific neuron cell bodies, and can beanalyzed. Thus, in a further embodiment, the methods of the inventionfurther comprise analyzing neurite outgrowth. As used herein, the phrase“neurite outgrowth” includes positive neurite outgrowth, neuriteoutgrowth inhibition, neurite outgrowth degradation, and other changesin neurite morphology. Such neurite morphology (“features”) computed foreach neuron may include, but are not limited to,

-   -   i. Number of nuclei; aggregate nucleus area, and intensity;    -   ii. Cell body size, shape and intensity; number of neurites;    -   iii. Aggregate neurite length, width, area, intensity, and        branch and cross point counts; distance of branch points from        the cell body surface;    -   iv. Intensity of reporters in cell body regions in additional        channels (e.g., neurotransmitters);    -   v. Mathematical combinations of all of the above; and    -   vi. Other parameters as discussed below

These features can also be used to categorize neurons into user-definedsubpopulations based on neuronal features. Analysis of neuronsubpopulations is not critical to neuronal analysis, but addssignificant value to the methods of the invention. The subpopulationscan be identified by the user using nucleus, cell body, and neuritefeatures, and by using information from reporters in additional imagingchannels, as described above.

In a preferred embodiment, the methods comprise contacting the cellpopulation comprising neurons with one or more test compounds, anddetermining the effect of the one or more test compounds on neuriteoutgrowth. The one or more test compounds can be of any nature,including, but not limited to, chemical and biological compounds andenvironmental samples. The one or more test compounds may also comprisea plurality of compounds, including but not limited to combinatorialchemical libraries and natural compound libraries. Contacting of thecell population with the one or more test compounds can occur before,after, and/or simultaneously with imaging of the cells, depending on theassay design. For example, in order to carry out kinetic screening, itis necessary to image the cells at multiple time points, and the usermay acquire such images before, at the time of, and after contacting ofthe cells with the test compound.

As will be understood by those of skill in the art, the various imagesobtained and the various processed images are preferably stored in adatabase (such as one included in a fluorescence detection device, asdiscussed below) that can be accessed by a user. The method thusprovides overlays of the cellular components (nucleus, cell body, andneurite) that can be displayed on top of the images, and computescell-level features, including but not limited to status and eventfeatures that, for example, access each cell's response to a testcompound added to a well containing the neurons. The status features aredetermined by comparing the value of each measured feature against areference level, either specified in the assay protocol or measuredversus one or more designated control (reference) wells on the plate.The event features for each cell are computed from its status featuresusing any appropriate techniques, including but not limited to Booleanlogic specified by the user (see below). During a well scan, thefeatures obtained from the reference well scan can be used to assignresponse, on the fly, to each neuron.

After all fields have been scanned in a well, the cell-level featurescan be aggregated and reported as well-level features. The well-levelfeatures include, but are not limited to, a statistical analysis of thefeatures for the cells analyzed and a separate statistical analysis ofthe features for subpopulations of cells. These subpopulations aredefined by the events. Events are flexible combinations of nucleus, cellbody, and neurite output features, created by the user through, forexample, Boolean logic.

In a second aspect, the present invention provides computer readablestorage media, for automatically carrying out the methods of theinvention on a detection device, such as a fluorescence detectiondevice. As used herein the term “computer readable medium” includesmagnetic disks, optical disks, organic memory, and any other volatile(e.g., Random Access Memory (“RAM”)) or non-volatile (e.g., Read-OnlyMemory (“ROM”)) mass storage system readable by the CPU. The computerreadable medium includes cooperating or interconnected computer readablemedium, which exist exclusively on the processing system or bedistributed among multiple interconnected processing systems that may belocal or remote to the processing system.

As used herein, “fluorescence detection device” means a device capableof carrying out the imaging required to carry out the invention,including, but not limited to, fluorescence microscopes; light scanningmicroscopy systems, including but not limited to point scanning,spinning disk, confocal, line scanning, and multi-photon microscopysystems; and epifluorescence microscopes. In a preferred embodiment, afluorescence microscope is used as part of an automated cell screeningsystem, which further comprises a fluorescence optical system with astage adapted for holding cells and a means for moving the stage, adigital camera, a light source, and a computer for receiving andprocessing the digital data from the digital camera, as well as forstoring the data in a database and displaying the data.

In a third aspect, the present invention provides detection devices(defined as above) that comprise computer readable storage media (alsoas described above) for carrying out the methods of the first aspect ofthe invention.

The present invention may be better understood with reference to theaccompanying examples that are intended for purposes of illustrationonly and should not be construed to limit the scope of the invention, asdefined by the claims appended hereto.

EXAMPLES Overview of One Embodiment of the Method

The flowchart shown in FIG. 1 outlines a sequence of image processingand analysis steps employed in one embodiment of the method. Each stepin the method is controlled by parameters that are found in the assayprotocol. This protocol can be modified by the user to configure themethod for many cell types and assays.

The neuronal specific labeling of the cell constitutes the minimumrequired fluorescent reporter/imaging channel. Other imaging channelscan be added as desired for a given application. For example, a nucleuschannel can be added if nucleus masks are required to fill holes in thecell body masks, or if the user desires to measure theneuronal/non-neuronal population ratio in a mixed culture of cells.Channels can be added to distinguish cells based on the presence ofreporters; for example, neurotransmitters or cellular dyes. Thedescription of the method below is of the most general case when allpossible imaging channels are being used.

The Channel 1 image (see FIG. 2) is processed first to create nucleusmasks. The nucleus features are extracted and the nuclei are validatedagainst criteria specified by the user in the algorithm's protocol.

The Channel 2 image (see FIG. 3) is then processed twice: once to obtainthe cell body masks and then to obtain the neurite masks. Thisprocessing is independent, using different methods and protocolparameters.

The cell body masks (see FIG. 4), are associated with the valid nucleiby co-localization. This association produces the valid nucleus countfor each neuron and a count of non-neuronal cells in the image field.The cell body features are extracted from the cell body masks and areused, along with the valid nucleus count, to validate cell bodies.

Modified (change of size) masks of the valid cell bodies are created forChannels 3-6 and are used to measure the intensities associated with thecell bodies in these channels. The cell bodies are then selected foranalysis based on the features measured in Channels 3-6 (this is called“gating”). After cell bodies are selected for analysis, nuclei are againvalidated to reject any that might belong to cell bodies that were notselected for analysis.

The neurite mask (see FIG. 5) is created from the Channel 2 image usinga threshold derived from the neuron cell body identification. Theneurite mask is skeletonized: reduced to single-pixel wide lines thatcorrespond to the medial axes of the neurites and the cell body masksare removed from the skeleton (see FIG. 6) to identify the points wherethe neurites are attached to the cell bodies (initial points). Criticalpoints where the skeleton branches or crosses itself are also identifiedand are removed from the skeleton to disassemble it into independentsegments (see FIG. 7).

The skeleton segments are reassembled in a process called “tracing,”which applies biologically derived rules (such as those described above)at each critical point to untangle the neurites and assign them to thecorrect neuron cell body. The traced neurites are independent (do notoverlap each other) and can be analyzed, and validated. Neurite featurescomputed for each neuron include neurite length, average width, area,intensity, the number of branch and cross points, and the distance ofbranch points from the neuron cell body surface.

The method completes its analysis of a current field by creatingoverlays of the cellular components (nucleus, cell body, and neurite)that can be displayed on top of the images, and computing cell-levelfeatures.

Included in the cell-level feature are status and event features thataccess each cell's response to the test compound added to the well. Thestatus features are determined by comparing the value of each measuredfeature against a reference level, either specified in the assayprotocol or measured by the method in designated control (reference)wells on the plate. The event features for each cell are computed fromits status features using Boolean logic specified by the user. Theprocessing of reference wells is not shown in the flowchart. Thealgorithm is used to analyze these first, if they are present on theplate, then sample wells are scanned. During the well scan, the featuresobtained from the reference well scan are used to assign response, onthe fly, to each neuron.

After all fields have been scanned in a well, the cell-level featuresare aggregated and reported as well-level features. The well-levelfeatures include a statistical analysis of the features for all thecells analyzed and a separate statistical analysis of the features foreach subpopulation of cells. These subpopulations are defined by theevents.

Overview of the Assay Parameters

This section describes each step of the method and its correspondingassay parameters. These assay parameters are listed in the table below.The processing steps in the table refer to the flowchart in FIG. 1.

Processing Controlling Step Parameter Description Use Reference wells:(0) do not use or (1) use Reference Wells Use Measure lengths and areasin: (0) pixels or Micrometers (1) micrometers Background Controlsbackground correction. Value is the Correction radius of area over whichthe slowly varying background is calculated. A value of zero means thatno background correction will be performed and analysis will useuncorrected images. Create Reject Nuc Reject nuclei that touch imageedges: 0 = Nucleus Border No, 1 = Yes Masks Objects Create Nuc Seg-Controls the segmentation of touching Nucleus mentation nuclei: Negativevalue = Use the intensity Masks method, 0 = Do not segment nuclei,Positive value = Use the shape method Create Nuc Smooth Degree of imagesmoothing (blurring) prior Nucleus Factor to nucleus identification: 0 =Do not smooth Masks image Create Cell Min Cell Common boundary (inpercent) between a Body Masks Body Nuc nucleus and a cell body must begreater than Common or equal to this value before the nucleus canBoundary be pasted into the cell body Create Cell Reject Cell Rejectcell bodies that touch image edges: Body Masks Body Border 0 = No, 1 =Yes Objects Create Cell Use Nuc For Use valid nuclei as seeds to segmentBody Masks Cell Body touching cell bodies: 0 = No, 1 = Yes (UseSegmenta- nuclei only), 2 = Yes (Use nuclei with cell tion bodyintensity or geometric methods) Create Cell Cell Body Controls thesegmentation of touching cell Body Masks Segmenta- bodies: Negativevalue = Use the intensity tion method, 0 = Do not segment cell bodies,Positive value = Use the shape method Create Cell Cell Body Degree ofimage smoothing (blurring) prior Body Masks Smooth to cell bodyidentification: 0 = Do not Factor smooth image Create Cell Cell BodyHalf-width (in pixels) of neurites to be Body Masks Neurite removed fromcell bodies during creation of Removal cell body masks: 0 = Do notremove neurites Size Create Cell Cell Body Number of pixels to modifythe size of the Body Masks Mask Modi- Ch2 cell body mask: Negative value= Make fier Ch2 mask smaller, 0 = Do not modify mask, Positive value =Make mask larger Create Neurite Degree of image smoothing (blurring)prior Neurite Smooth to neurite identification: 0 = Do not smooth MasksFactor image Create Neurite Adjusts the neurite threshold intensityNeurite Threshold relative to that used to identify cell bodies. A MasksModifier negative value identifies dimmer neurites. The thresholdderived using the neurite identification modifier is bounded by 1 and4095. Create Neurite Method used to detect neurites: 1 = uniform,Neurite Detect 2 = binomial, 3 = median, and 4 = top hat Masks MethodCreate Neurite Half-width (in pixels) of the largest neurites NeuriteDetect to be detected in the image: 0 = Do not Masks Radius detectneurites Trace Reject Reject neurites that touch more than one NeuritesMultiply- neuron: 0 = neurite will be assigned to a cell Traced body, 1= neurite will not be analyzed Neurites Trace Neurite Number of pixelsused to compute the Neurites Direction direction of each neurite segmentend Pixel Count Trace Min Branch Merge each pair of neighboring branchNeurites Point points that are separated by fewer than this Separationnumber of pixels into a single cross point 0 = Pixel Count Do not mergeneighboring branch points Make Neurite Point Selects display of neuritebranch and cross Overlays Display points: (0) display all, (1) displayonly Mode branch points, (2) display only cross points Modify Cell CellBody Number of pixels to erode or dilate the cell Body Masks Mask Modi-body mask in Ch3 used for gating (negative fier Ch3 value makes masksmaller). Modify Cell Cell Body Number of pixels to erode or dilate thecell Body Masks Mask Modi- body mask in Ch4 used for gating (negativefier Ch4 value makes mask smaller). Modify Cell Cell Body Number ofpixels to erode or dilate the cell Body Masks Mask Modi- body mask inCh5 used for gating (negative fier Ch5 value makes mask smaller). ModifyCell Cell Body Number of pixels to erode or dilate the cell Body MasksMask Modi- body mask in Ch6 used for gating (negative fier Ch6 valuemakes mask smaller).Cell Body Mask Creation

In some cases, cell body mask creation is complicated by the holes (darkregions) in the cytoplasm image (see FIG. 3) caused by the displacementof the reporters by the nucleus (see FIG. 2). If these holes arepresent, they are preferably filled. Filling holes in the cell bodymasks involves the addition of a nucleus-imaging channel (e.g., Channel1). The masks of objects from Channel 1 are pasted into Channel 2.However, since some nuclei in Channel 1 may not belong to neurons (i.e.,are located outside neuronal cell bodies), a co-localization test isperformed between Channels 1 and 2 masks before pasting. The assayparameter that configures this test is MinCellBodyNucCommonBoundary.

After cell body holes are filled, any neurites that might be attached tothe cell body masks are stripped off using a sequence of erosions anddilations (see FIG. 4). The CellBodyNeuriteRemovalSize assay parameterspecifies the stopping criterion for the removal of neurites from thecell body masks.

Following neurite removal, the Channel 2 objects are segmented toseparate cell bodies that might touch or overlap. Segmentation iscontrolled by two assay parameters, CellBodySegmentation andUseNucForCellBodySegmentation. The former specifies how cell bodyinformation (e.g., boundary shape or internal intensity distribution) isused in segmentation. The latter indicates that nucleus masks areavailable and that these should be used to divide the Channel 2 objectsinto segments.

The features of the Channel 2 objects are then extracted and comparedwith the neuron cell body validation criteria in the assay protocol.Prior to validation of Channel 1 and Channel 2 objects the method checksif they are touching the edge of the image. Border-touching objects areaccepted or rejected as specified by the RejectNucBorderObjects andRejectCellBodyBorderObjects assay parameters.

If desired by the user, the size of each valid cell body mask can bemodified as specified by the CellBodyMaskModifierCh2 assay parameter.

Neurite Mask Creation

Neurite masks are derived from the Channel 2 image using the processingsteps described in FIG. 8. The neurites are first detected in theChannel 2 gray-scale image. This is an image processing step thatremoves large objects from the image, making it easier to identify finerones. Neurite detection is controlled by the NeuriteDetectRadius andNeuriteDetectMethod assay parameters. After detection the neurite maskis created (see FIG. 5) using a threshold determined by theuser-specified NeuriteThresholdModifier assay parameter and thethreshold value used to identify cell bodies. Depending on the degreeand method of detection, the neurite mask may contain cell bodies.

The neurite mask is skeletonized, which reduces each neurite to asingle-pixel-wide line along the medial axis of the neurite. Theskeleton will be used for neurite tracing, to measure neurite length,and to identify branch and cross points. The neurite mask will be usedto measure neurite width, area, and for neurite intensity measurements.The cell body masks are removed from both the neurite mask and itsskeleton (see FIG. 6) before this analysis.

Neurite Tracing

Tracing is the process employed by the method to untangle the neuriteskeleton and assign individual neurites to cell bodies. The flowchartfor the neurite tracing process is shown in FIG. 9. Neurite tracingbegins with identification of the skeleton's initial and criticalpoints. The initial points are the skeleton pixels that touch a cellbody surface. These are identified by conditionally dilating the cellbody mask by one pixel onto the neurite skeleton mask. Critical pointsare branch and cross points in the neurite skeleton. They can beidentified by counting the number of 8-connected neighbors for eachskeleton pixel (8-connected neighbors are skeleton mask pixels in a 3×3array centered on the test pixel). The skeleton pixel belongs to acritical point if it has more than two 8-connected connected neighbors(see FIG. 10). The neurite skeleton is then disassembled intoindependent segments by removing the critical points (see FIG. 7).

The initial points, critical points, and skeleton segments are thenre-assembled as described in the middle of the flowchart in FIG. 9. Thefirst initial point of the skeleton is selected and its segment isattached. This forms the trunk of the first skeleton tree. If thesegment touches a critical point, this is added to the trunk. Thecritical point is analyzed to determine whether it is a cross point or abranch point, with a cross point being the vertex of four neuritesegments and a branch point being the vertex of three. The next segmentadded to the trunk is chosen by comparing the directions of the criticalpoint's outgoing segments to that of the incoming one. These directionsare computed using a linear least squares fit to the N pixels of eachsegment that are closest to the critical point. The number of pixels, N,used in this fit is specified by the NeuriteDirectionPixelCount assayparameter.

The outgoing segment that is added to the trunk is the one thatminimizes the change in direction, or kink angle (see FIG. 11). The kinkangle is measured between an outgoing segment and the projection of theincoming segment, the dashed line in FIG. 11. If no outgoing segmentmeets the kink angle criterion, the trunk is terminated. If the criticalpoint is a branch point, the outgoing segment not added to the trunk isstored for future use as a branch. When the trunk is completely traced,the stored branches are processed in a similar fashion until noneremain. Then another initial point is selected and the process continuesuntil a skeleton tree has been created for each initial point.

If a neurite touches more than one cell body, it will have more than oneinitial point and will be traced multiple times. This is illustrated inFIG. 12, which shows two skeleton trees (solid and dotted) that werecreated for the same neurite. Because of the maximum allowed kink angle,trees with branches will not be identical. In this case the tree withthe largest length (solid in FIG. 12) is kept and the other copies aredeleted. If there are no branches, the multiply traced skeleton treeswill be identical and one of the cell bodies is chosen at random to bethe owner of the neurite. The user can specify what happens to multiplytraced neurites by setting the RejectMultiplyTracedNeurites assayparameter.

Neurite Analysis

The steps followed in the analysis of the neurites are illustrated inFIG. 13. As a result of tracing, the neurite skeleton has beenre-assembled into a set of independent trees that do not actuallyoverlap at cross points. These trees can be analyzed for length andcritical point counts. However, since the skeleton is only one pixelwide, these trees cannot be used to measure area and intensity. Thesefeatures must be measured using the neurite mask.

Unlike the traced skeleton, the neurite mask still retains its originalcontinuity and must be separated into independent parts before it can beanalyzed. This segmentation is done using a variation of the watershedmethod, with the distance transform of the neurite mask used as thegradient and the traced skeleton as the seed. This is illustrated inFIG. 14. The watershed method used seals off the ends of neurites thatdo not contain the traced skeleton, and maintains the separation of theneurites at cross points.

Output Features

Individual Object Features

The method measures the features of each individual nucleus, cell body,and neurite. The features of these individual objects are used forobject validation and include the following:

Nucleus features (Used for nucleus validation): Area, ShapeP2A(Perimiter² divided by (4π times Area) ), ShapeLWR (Length divided byWidth), AvgInten (Average Intensity), and TotalInten (Total Intensity)measured in Channel 1;

Cell body features (Used for cell body validation and gating): Area,ShapeP2A, ShapeLWR, AvgInten, and TotalInten measured in Channel 2;AvgInten and TotalInten within modified cell body masks measured inChannels 3-6; and

Neurite features (Used for neurite validation):Length, Width, Cross andBranch Point Counts, AvgInten, TotalInten, and VarInten (Variation inIntensity) measured in Channel 2.

Cell-Level Features

The features of the individual objects are aggregated to obtaincell-level features for each neuron. This process involves computingcounts, totals, averages, maxima, etc. for the nuclei and neurites,which can occur in multiple instances within each cell. Cell bodyfeatures are not aggregated because there is only one cell body percell.

The cell-level features reported by the method for each cell analyzedare listed in the table below. Included in these features are Event andStatus features. These will be described in following sections.

Feature Description Cell# Unique cell identification for the plate TopStarting position of cell body bounding box along y Left Startingposition of cell body bounding box along x Width Cell body bounding boxwidth Height Cell body bounding box height XCentroid Center of the cellbody along the X axis YCentroid Center of the cell body along the Y axisEventTypeProfile Cell event type EventType1Status Status of EventType1:0 = Event did not occur, 1 = Event occurred EventType2Status Status ofEventType2: 0 = Event did not occur, 1 = Event occurred EventType3StatusStatus of EventType3: 0 = Event did not occur, 1 = Event occurredCellBodyNucCount Count of the valid nuclei assigned to the cell bodyCellBodyNucCount- CellBodyNucCount status: 0 = No response, 1 = StatusHigh response CellBodyNucTotal- Total area (in pixels or micrometers) ofthe valid Area nuclei assigned to the cell body CellBodyNucTotal-CellBodyNucTotalArea status: 0 = No response, 1 = AreaStatus Highresponse CellBodyNucTotal- Total intensity of the pixels in the validnuclei Inten assigned to the cell body CellBodyNucTotal-CellBodyNucTotalIntenStatus status: 0 = No IntenStatus response, 1 =High response CellBodyNucAvg- Average intensity of the pixels in thevalid nuclei Inten assigned to the cell body CellBodyNucAvg-CellBodyNucAvgInten status: 0 = No response, 1 = IntenStatus Highresponse CellBodyArea Area (in pixels or micrometers) of cell body (notincluding neurites, if any) CellBodyAreaStatus CellBodyArea status: 0 =No response, 1 = High response CellBodyShapeP2A Shape measure of thecell body based on ratio of the perimeter squared to 4PI*area(CellBodyShapeP2A = 1 for circular cell bodies) CellBodyShape-CellBodyShapeP2A status: 0 = No response, 1 = P2AStatus High responseCellBodyShape- Shape measure of the cell body based on the length- LWRto-width ratio of the object-aligned bounding box CellBodyShape-CellBodyShapeLWR status: 0 = No response, 1 = LWRStatus High responseCellBodyTotalInten Total intensity of the pixels in the cell bodyCellBodyTotalInten- CellBodyTotalInten status: 0 = No response, 1 =Status High response CellBodyAvgInten Average intensity of the pixels inthe cell body CellBodyAvgInten- CellBodyAvgInten status: 0 = Noresponse, 1 = Status High response NeuriteCount Number of neuritesassociated with the selected neuron NeuriteCountStatus Defines neuritecount status: (0) is less than or equal to the neurite count thresholdand (1) is greater than the neurite count threshold NeuriteTotalLengthTotal neurite length for the selected neuron. NeuriteTotalLength-Defines total neurite length status: (0) is less than or Status equal tothe total neurite length threshold and (1) is greater than the totalneurite length threshold NeuriteAvgLength Average length (in pixels ormicrometers) of the neurites assigned to the cell body NeuriteAvgLength-NeuriteAvgLength status: 0 = No response, 1 = Status High responseNeuriteMaxLength- Maximum length with branches (in pixels or micro-WithBranches meters) of the neurites assigned to the cell bodyNeuriteMaxLength- NeuriteMaxLengthWithBranches status: 0 = NoWithBranchesStatus response, 1 = High response NeuriteMaxLength- Maximumlength without branches (in pixels or WithoutBranches micrometers) ofthe neurites assigned to the cell body NeuriteMaxLength-NeuriteMaxLengthWithoutBranches status: 0 = No WithoutBranches-response, 1 = High response Status NeuriteTotalArea Total area (inpixels or micrometers) of the neurites assigned to the cell bodyNeuriteTotalArea- NeuriteTotalArea status: 0 = No response, 1 = HighStatus response NeuriteAvgWidth Average width (in pixels or micrometers)of the neurites assigned to the cell body NeuriteAvgWidth-NeuriteAvgWidth status: 0 = No response, 1 = High Status responseNeuriteTotalInten Total intensity of pixels in the neurites assigned tothe cell body NeuriteTotalInten- NeuriteTotalInten status: 0 = Noresponse, 1 = High Status response NeuriteAvgInten Average intensity ofthe pixels in the neurites assigned to the cell body NeuriteAvgInten-NeuriteAvgInten status: 0 = No response, 1 = High Status responseNeuriteVarInten Standard deviation of the intensity of the pixels in theneurites assigned to the cell body NeuriteVarInten- NeuriteVarIntenstatus: 0 = No response, 1 = High Status response BranchPointTotal-Total number of branch points in the neurites Count assigned to the cellbody BranchPointTotal- BranchPointTotalCount status: 0 = No response, 1= CountStatus High response BranchPointAvg- Average number of branchpoints per neurite Count assigned to the cell body BranchPointAvg-BranchPointAvgCount status: 0 = No response, 1 = CountStatus Highresponse BranchPointAvg- Average distance (in pixels or micrometers) ofthe DistFromCellBody branch points from the cell body surface in theneurites assigned to the cell body BranchPointAvg-BranchPointAvgDistFromCellBody status: 0 = No DistFromCell- response, 1= High response BodyStatus CrossPointTotal- Total number of cross pointsin the neurites Count assigned to the cell body CrossPointTotal-CrossPointTotalCount status: 0 = No response, 1 = CountStatus Highresponse CrossPointAvg- Average number of cross points per neuriteassigned Count to the cell body CrossPointAvg- CrossPointAvgCountstatus: 0 = No response, 1 = CountStatus High response TotalIntenCh3Total intensity in Ch3 of the pixels in the modified cell body maskTotalIntenStatusCh3 TotalIntenCh3 status: 0 = No response, 1 = Highresponse AvgIntenCh3 Average intensity in Ch3 of the pixels in themodified cell body mask AvgIntenStatusCh3 AvgIntenCh3 status: 0 = Noresponse, 1 = High response TotalIntenCh4 Total intensity in Ch4 of thepixels in the modified cell body mask TotalIntenStatusCh4 TotalIntenCh4status: 0 = No response, 1 = High response AvgIntenCh4 Average intensityin Ch4 of the pixels in the modified cell body mask AvgIntenStatusCh4AvgIntenCh4 status: 0 = No response, 1 = High response TotalIntenCh5Total intensity in Ch5 of the pixels in the modified cell body maskAvgIntenCh5 Average intensity in Ch5 of the pixels in the modified cellbody mask TotalIntenCh6 Total intensity in Ch6 of the pixels in themodified cell body mask AvgIntenCh6 Average intensity in Ch6 of thepixels in the modified cell body maskStatus Features

When each of the primary cell-level features described above is measuredover a sample of many untreated cells, it will likely form adistribution similar to the one shown on the left side of FIG. 15 (solidcurve). If this same cell feature is measured for a sample of treatedcells, its distribution of values may look like the curve on the rightside of FIG. 15 (dashed curve). In this case the cells respondedpositively to treatment, which shifted the curve to the right. Using thetreated and untreated distributions, a response level can be establishedfor each cell feature that will identify the cells that have respondedto treatment. The methods available for setting the response levels havebeen described previously.

Associated with each cell feature is a status feature that indicates thecell's response: If the value of a cell feature is above the responselevel, the status feature associated with the cell feature is set to 1(true). If the value of a cell feature is below or equal to the level,the status feature is set to 0 (false). The status features are reportedwith the Cell-Level Features.

Event Features

The user can define events for each cell using Boolean operators tocombine the status features described above. Exemplary Boolean operatorsare listed in the following table:

Logic Operators NOT AND AND NOT OR OR NOT XOR NAND NORExamples of events includeEvent=Neurite Count OR Neurite Total LengthEvent=Avg Inten Ch3 AND NOT Avg Inten Ch4The event features are reported with the cell-level features.Well-Level Features

The power of High-Content Analysis is that it can identify individualcells in a sample that have responded to treatment in a certain way, andcan then compute statistical summaries of the features for just thatgroup of cells (subpopulation analysis). The response to treatment thatdefines a subpopulation is an event. This response can be simple,directly linked to the change of one cell feature, or complex, acombination of the changes in several cell features.

The well-level features are summarized in the table below. Populationfeatures include statistical summaries of each cell-level feature (Mean,Standard Deviation, Standard Error, Coefficient of Variation, and %Responding Cells) for all the cells analyzed in the well. Cell countsand relative abundances are also reported for each of the subpopulationsthat were defined.

Subpopulation features include statistical summaries of the cell-levelfeature for only those cells that belong to the subpopulation. Cellcounts and relative abundances are also reported for the subpopulationcells that also belong to each of the other subpopulations.

Population Subpopulation N Feature Type (All Selected Cells) (N = 1, 2,and 3) Cell Feature Mean Mean Statistics Standard Deviation StandardDeviation (For each Cell- Standard Error Standard Error Level Feature)Coefficient of Variation Coefficient of Variation % Responding Cells %Responding Cells Event Type 1 Count of selected cells in Count of Subpop1 cells in Statistics Subpop 1 Subpop N % of selected cells in % ofSubpop 1 cells in Subpop 1 Subpop N Event Type 2 Count of selected cellsin Count of Subpop 2 cells in Statistics Subpop 2 Subpop N % of selectedcells in % of Subpop 2 cells in Subpop 2 Subpop N Event Type 3 Count ofselected cells in Count of Subpop 3 cells in Statistics Subpop 3 SubpopN % of selected cells in % of Subpop 3 cells in Subpop 3 Subpop NThe definitions of the percentages in the table are as follows:% Subpop 1 Cells=100%×(# of Subpop 1 cells)/(# of Selected Cells)% Subpop 1 Cells in Subpop N=100%×(# of Subpop 1 Cells in Subpop N)/(#of Subpop 1 Cells)

For completeness the counts and percentages of a subpopulation withinitself (e.g., # of Subpop 1 cells in Subpop 1 and % Subpop 1 Cells inSubpop 1) are reported, even though these feature contain no newinformation.

1. An automated method for cell body extension analysis, comprising: (a)obtaining cell body images and cell body extension images from multiplecells in a population of cells comprising cells with cell bodyextensions, wherein the cells and cell body extensions are labeled withreporters, and wherein the cell body images and the cell body extensionimages are obtained using a fluorescence detection device to detectsignals emitted by the reporters; (b) processing the cell body images tocreate cell body masks and to validate cell bodies in the cell bodymasks, and processing the cell body extension images to create cell bodyextension masks, which are processed to produce cell body extensionskeletons; (c) untangling cell body extensions from multiple cells,wherein the untangling comprises: (i) identifying end points on the cellbody extension skeletons; (ii) identifying and removing critical pointsfrom the cell body extension skeletons; and (iii) tracing the end pointsand the critical points to untangle cell body extensions in the cellbody extension skeletons; and (d) assigning untangled cell bodyextensions to the validated cell bodies.
 2. The method of claim 1,wherein the population of cells comprise neurons, and wherein the cellbody extensions comprise neurites.
 3. The automated method of claim 2,wherein validating cell bodies in the cell body mask comprisessegmenting objects in the cell body mask to separate cell bodies.
 4. Theautomated method of claim 2, wherein validating cell bodies in the cellbody mask comprises removing neurites from the cell body mask.
 5. Themethod of claim 2,wherein identifying and removing critical points fromthe neurite skeleton comprises producing a plurality of neurite skeletonsegments.
 6. The method of claim 2, wherein tracing the end points andcritical points to untangle the neurites comprises: (i) selecting aninitial point and attaching its neurite skeleton segment to form a firstneurite skeleton segment; (ii) adding to the first neurite skeletonsegment a first critical point touching the first neurite skeletonsegment; (iii) adding to the first critical point a second neuriteskeleton segment that minimizes a kink angle at the first critical pointand which is in the overall growth direction of the neurite; and (iv)repeating steps (ii)-(iii) a desired number of times, wherein thetracing serves to assign neurites to validated cell bodies.
 7. Themethod of claim 2, wherein assigning neurites to validated cell bodiescomprises (i) tracing the neurite skeletons using available initialpoints, critical points and neurite skeleton segments; (ii) tracing aneurite skeleton for each validated cell body that a neurite touches;and (iii) assigning the neurite to the validated cell body on which theneurite would have the overall longest length.
 8. The method of claim 2,further comprising obtaining a nuclear image.
 9. The method of claim 8further comprising processing the nuclear image to obtain a nuclearmask.
 10. The method of claim 9, further comprising segmenting nuclearmask objects.
 11. The method of claim 10, wherein processing the cellbody image to create a cell body mask creates a combined cellbody/neurite mask.
 12. The method of claim 11, further comprisingfilling holes in cell bodies in the combined cell body/neurite mask. 13.The method of claim 12, wherein filling holes in cell bodies comprises(i) creating test ring around each nucleus mask object; (ii) measuringcontiguities between the test rings and cell body mask objects; and(iii) pasting nucleus mask objects that pass a contiguity test into thecell body mask.
 14. The method of claim 11 further comprising removingneurites from the combined cell body/neurite mask to create cell bodyobjects.
 15. The method of claim 13, wherein validating cell bodies inthe combined cell body/neurite mask comprises segmenting cell body maskobjects.
 16. The method of claim 14, wherein cell body mask objects thatdo not co-localize with a nucleus are removed from the cell body mask.17. The method of claim 12, further comprising filling holes in theneurite mask prior to skeletonization.
 18. The method of claim 2,wherein obtaining the neurite skeleton mask comprises one or more of:(i) pruning the neurite skeletons to remove single-pixel branches; (ii)thinning the neurite skeletons to one pixel thick at all points; (iii)identifying the critical points as crossing or a branch points based ona number of neurite skeleton segments that meet at each point; and (iv)smoothing discontinuities at neurite crossing points so these points areidentified as a single cross point instead of a pair of closely spacedbranch points.
 19. The method of claim 2, wherein the method farthercomprises contacting the population of cells comprising neurons with oneor more test compounds, and analyzing neurite outgrowth of the untangledneurites.
 20. The method of claim 19, wherein analyzing neuriteoutgrowth comprises determining an effect of one or more test compoundson neurite outgrowth.
 21. The method of claim 2, wherein the methodfarther comprises identification of subpopulations of cells in the cellpopulation.